{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Linear Regression Part 3"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "import yfinance as yf\n",
        "yf.pdr_override()"
      ],
      "outputs": [],
      "execution_count": 1,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-08-27T19:15:51.758Z",
          "iopub.execute_input": "2022-08-27T19:15:51.766Z",
          "shell.execute_reply": "2022-08-27T19:15:52.490Z",
          "iopub.status.idle": "2022-08-27T19:15:52.498Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# input\n",
        "symbol = 'AMD'\n",
        "start = '2014-01-01'\n",
        "end = '2018-08-27'\n",
        "\n",
        "# Read data \n",
        "dataset = yf.download(symbol,start,end)['Adj Close']\n",
        "\n",
        "# View Columns\n",
        "dataset.head()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "execute_result",
          "execution_count": 2,
          "data": {
            "text/plain": "Date\n2014-01-02    3.95\n2014-01-03    4.00\n2014-01-06    4.13\n2014-01-07    4.18\n2014-01-08    4.18\nName: Adj Close, dtype: float64"
          },
          "metadata": {}
        }
      ],
      "execution_count": 2,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-08-27T19:15:52.504Z",
          "iopub.execute_input": "2022-08-27T19:15:52.507Z",
          "shell.execute_reply": "2022-08-27T19:15:53.307Z",
          "iopub.status.idle": "2022-08-27T19:15:53.312Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = pd.concat([dataset, dataset.shift(), dataset.shift(2),dataset.shift(3),dataset.shift(4),dataset.shift(5), dataset.shift(6),dataset.shift(7),dataset.shift(8),dataset.shift(9),dataset.shift(10)], axis=1).dropna() \n",
        "dataset.columns = list(range(0,11))\n",
        "dataset.rename(columns={0:'actual_stock_price'},inplace=True)\n",
        "dataset.head()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 3,
          "data": {
            "text/plain": "            actual_stock_price     1     2     3     4     5     6     7  \\\nDate                                                                       \n2014-01-16                4.38  4.47  4.30  4.13  4.17  4.09  4.18  4.18   \n2014-01-17                4.18  4.38  4.47  4.30  4.13  4.17  4.09  4.18   \n2014-01-21                4.17  4.18  4.38  4.47  4.30  4.13  4.17  4.09   \n2014-01-22                3.67  4.17  4.18  4.38  4.47  4.30  4.13  4.17   \n2014-01-23                3.62  3.67  4.17  4.18  4.38  4.47  4.30  4.13   \n\n               8     9    10  \nDate                          \n2014-01-16  4.13  4.00  3.95  \n2014-01-17  4.18  4.13  4.00  \n2014-01-21  4.18  4.18  4.13  \n2014-01-22  4.09  4.18  4.18  \n2014-01-23  4.17  4.09  4.18  ",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>actual_stock_price</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n      <th>8</th>\n      <th>9</th>\n      <th>10</th>\n    </tr>\n    <tr>\n      <th>Date</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2014-01-16</th>\n      <td>4.38</td>\n      <td>4.47</td>\n      <td>4.30</td>\n      <td>4.13</td>\n      <td>4.17</td>\n      <td>4.09</td>\n      <td>4.18</td>\n      <td>4.18</td>\n      <td>4.13</td>\n      <td>4.00</td>\n      <td>3.95</td>\n    </tr>\n    <tr>\n      <th>2014-01-17</th>\n      <td>4.18</td>\n      <td>4.38</td>\n      <td>4.47</td>\n      <td>4.30</td>\n      <td>4.13</td>\n      <td>4.17</td>\n      <td>4.09</td>\n      <td>4.18</td>\n      <td>4.18</td>\n      <td>4.13</td>\n      <td>4.00</td>\n    </tr>\n    <tr>\n      <th>2014-01-21</th>\n      <td>4.17</td>\n      <td>4.18</td>\n      <td>4.38</td>\n      <td>4.47</td>\n      <td>4.30</td>\n      <td>4.13</td>\n      <td>4.17</td>\n      <td>4.09</td>\n      <td>4.18</td>\n      <td>4.18</td>\n      <td>4.13</td>\n    </tr>\n    <tr>\n      <th>2014-01-22</th>\n      <td>3.67</td>\n      <td>4.17</td>\n      <td>4.18</td>\n      <td>4.38</td>\n      <td>4.47</td>\n      <td>4.30</td>\n      <td>4.13</td>\n      <td>4.17</td>\n      <td>4.09</td>\n      <td>4.18</td>\n      <td>4.18</td>\n    </tr>\n    <tr>\n      <th>2014-01-23</th>\n      <td>3.62</td>\n      <td>3.67</td>\n      <td>4.17</td>\n      <td>4.18</td>\n      <td>4.38</td>\n      <td>4.47</td>\n      <td>4.30</td>\n      <td>4.13</td>\n      <td>4.17</td>\n      <td>4.09</td>\n      <td>4.18</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 3,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "shell.execute_reply": "2022-08-27T19:15:53.367Z",
          "iopub.status.busy": "2022-08-27T19:15:53.317Z",
          "iopub.execute_input": "2022-08-27T19:15:53.321Z",
          "iopub.status.idle": "2022-08-27T19:15:53.338Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the data into training and test data sets. \n",
        "train = dataset.head(len(dataset)-10)\n",
        "test = dataset.tail(10)"
      ],
      "outputs": [],
      "execution_count": 4,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-08-27T19:15:53.343Z",
          "iopub.execute_input": "2022-08-27T19:15:53.346Z",
          "iopub.status.idle": "2022-08-27T19:15:53.352Z",
          "shell.execute_reply": "2022-08-27T19:15:53.370Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import the Linear Regression model from scikit-learn.\n",
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "# Create the model object.\n",
        "model = LinearRegression()\n",
        "\n",
        "# Train the model on the training data.\n",
        "model.fit(train[list(range(1,11))],train['actual_stock_price'])\n",
        "\n",
        "# Make predictions on the test data.\n",
        "test['predictions'] = model.predict(test[list(range(1,11))])"
      ],
      "outputs": [],
      "execution_count": 5,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "shell.execute_reply": "2022-08-27T19:15:53.997Z",
          "iopub.status.busy": "2022-08-27T19:15:53.357Z",
          "iopub.execute_input": "2022-08-27T19:15:53.360Z",
          "iopub.status.idle": "2022-08-27T19:15:54.003Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize the actual stock price and the predicted stock price over the time period.\n",
        "plt.figure(figsize=(12,7))\n",
        "ax = test[['actual_stock_price','predictions']].plot(figsize=(12,8))\n",
        "plt.xlabel('Date', fontsize=12)\n",
        "plt.ylabel('Stock Price', fontsize=12)\n",
        "plt.title('Actual and Predicted Stock Prices', fontsize=15)\n",
        "plt.grid()\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 864x504 with 0 Axes>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 864x576 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "execution_count": 6,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "shell.execute_reply": "2022-08-27T19:15:54.160Z",
          "iopub.status.busy": "2022-08-27T19:15:54.007Z",
          "iopub.execute_input": "2022-08-27T19:15:54.010Z",
          "iopub.status.idle": "2022-08-27T19:15:54.151Z"
        }
      }
    }
  ],
  "metadata": {
    "kernel_info": {
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.6.13",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "kernelspec": {
      "argv": [
        "C:/Users/Tin Hang/Anaconda3\\python.exe",
        "-m",
        "ipykernel_launcher",
        "-f",
        "{connection_file}"
      ],
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "nteract": {
      "version": "0.28.0"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}